RT info:eu-repo/semantics/conferenceObject
T1 Supervised Machine Learning Techniques for Quality of Transmission Assessment in Optical Networks
A1 Mata, Javier
A1 Miguel Jiménez, Ignacio de
A1 Durán Barroso, Ramón José
A1 Aguado Manzano, Juan Carlos
A1 Merayo Álvarez, Noemí
A1 Ruiz Pérez, Lidia
A1 Fernández Reguero, Patricia
A1 Lorenzo Toledo, Rubén Mateo
A1 Abril Domingo, Evaristo José
A1 Tomkos, Ioannis
K1 Aprendizaje automático
K1 Red óptica
K1 Machine learning
K1 Optical network
AB We propose and compare a number of machine learning models to classify unestablished lightpaths into high or low quality of transmission (QoT) categories in impairment-aware wavelength-routed optical networks. The performance of these models is evaluated in long haul communication networks and compared to previous proposals. Results show that, especially random forests and bagging trees approaches, significantly reduce the required computing time to classify the QoT of a given lightpath, while accuracy remains around 99.9%.
PB Institute of Electrical and Electronics Engineers (IEEE)
SN 978-1-5386-6605-0
YR 2018
FD 2018
LK http://uvadoc.uva.es/handle/10324/33520
UL http://uvadoc.uva.es/handle/10324/33520
LA eng
NO 2018 20th International Conference on Transparent Optical Networks (ICTON), 1-5 July 2018, Bucharest, Romania
NO Producción Científica
DS UVaDOC
RD 18-abr-2024